Comparing Dataset A and Dataset B

Brought to you by YData

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	442	437
Missing cells (%)	8.3%	8.2%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 93 (20.9%) missing values	`Age` has 85 (19.1%) missing values	Missing
`Cabin` has 348 (78.0%) missing values	`Cabin` has 351 (78.7%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 310 (69.5%) zeros	`SibSp` has 301 (67.5%) zeros	Zeros
`Parch` has 340 (76.2%) zeros	`Parch` has 335 (75.1%) zeros	Zeros
`Fare` has 6 (1.3%) zeros	`Fare` has 8 (1.8%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2025-09-23 16:02:45.965654	2025-09-23 16:02:48.141995
Analysis finished	2025-09-23 16:02:48.139123	2025-09-23 16:02:50.267723
Duration	2.17 seconds	2.13 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	443.85874	467.34978

	Dataset A	Dataset B
Minimum	1	3
Maximum	891	890
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	3
5-th percentile	50.5	51.25
Q1	211.25	250.25
median	447.5	481.5
Q3	668.5	683.5
95-th percentile	851.5	845.75
Maximum	891	890
Range	890	887
Interquartile range (IQR)	457.25	433.25

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	258.60845	254.54825
Coefficient of variation (CV)	0.58263684	0.54466326
Kurtosis	-1.2003823	-1.1902288
Mean	443.85874	467.34978
Median Absolute Deviation (MAD)	228	216
Skewness	0.034350528	-0.10317086
Sum	197961	208438
Variance	66878.333	64794.812
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
124	1	0.2%
477	1	0.2%
569	1	0.2%
642	1	0.2%
523	1	0.2%
546	1	0.2%
259	1	0.2%
111	1	0.2%
530	1	0.2%
99	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
499	1	0.2%
792	1	0.2%
192	1	0.2%
157	1	0.2%
582	1	0.2%
344	1	0.2%
30	1	0.2%
887	1	0.2%
748	1	0.2%
99	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
4	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
16	1	0.2%
19	1	0.2%

Value	Count	Frequency (%)
3	1	0.2%
5	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
14	1	0.2%
16	1	0.2%
22	1	0.2%
23	1	0.2%
24	1	0.2%

Value	Count	Frequency (%)
3	1	0.2%
5	1	0.2%
8	1	0.2%
9	1	0.2%
10	1	0.2%
14	1	0.2%
16	1	0.2%
22	1	0.2%
23	1	0.2%
24	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
4	1	0.2%
7	1	0.2%
9	1	0.2%
10	1	0.2%
12	1	0.2%
13	1	0.2%
16	1	0.2%
19	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	273
1	173

0	278
1	168

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	0
2nd row	0	0
3rd row	1	1
4th row	0	1
5th row	0	0

Common Values

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring characters

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	273	61.2%
1	173	38.8%

Value	Count	Frequency (%)
0	278	62.3%
1	168	37.7%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	252
1	105
2	89

3	244
2	101
1	101

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	2	2
2nd row	3	2
3rd row	1	3
4th row	3	1
5th row	1	2

Common Values

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	244	54.7%
2	101	22.6%
1	101	22.6%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	244	54.7%
2	101	22.6%
1	101	22.6%

Most occurring characters

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	244	54.7%
2	101	22.6%
1	101	22.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	244	54.7%
2	101	22.6%
1	101	22.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	244	54.7%
2	101	22.6%
1	101	22.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	252	56.5%
1	105	23.5%
2	89	20.0%

Value	Count	Frequency (%)
3	244	54.7%
2	101	22.6%
1	101	22.6%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	67
Median length	49	48
Mean length	26.798206	27.109865
Min length	13	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	11952	12091
Distinct characters	60	59
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Renouf, Mr. Peter Henry	Gaskell, Mr. Alfred
2nd row	Doharr, Mr. Tannous	Carbines, Mr. William
3rd row	Sagesser, Mlle. Emma	Gilnagh, Miss. Katherine "Katie"
4th row	Lahoud, Mr. Sarkis	Thayer, Mrs. John Borland (Marian Longstreth Morris)
5th row	Nicholson, Mr. Arthur Ernest	Sedgwick, Mr. Charles Frederick Waddington

Value	Count	Frequency (%)
mr	258	14.3%
miss	91	5.0%
mrs	68	3.8%
william	37	2.1%
john	24	1.3%
henry	20	1.1%
master	18	1.0%
thomas	12	0.7%
george	12	0.7%
mary	11	0.6%
Other values (891)	1251	69.4%

Value	Count	Frequency (%)
mr	261	14.4%
miss	88	4.8%
mrs	64	3.5%
william	33	1.8%
master	25	1.4%
john	18	1.0%
charles	15	0.8%
thomas	15	0.8%
henry	14	0.8%
george	11	0.6%
Other values (913)	1272	70.0%

Most occurring characters

Value	Count	Frequency (%)
	1357	11.4%
r	957	8.0%
e	848	7.1%
a	826	6.9%
n	659	5.5%
i	659	5.5%
s	626	5.2%
M	569	4.8%
l	530	4.4%
o	491	4.1%
Other values (50)	4430	37.1%

Value	Count	Frequency (%)
	1371	11.3%
r	964	8.0%
a	839	6.9%
e	837	6.9%
i	699	5.8%
s	646	5.3%
n	635	5.3%
l	557	4.6%
M	556	4.6%
o	487	4.0%
Other values (49)	4500	37.2%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	11952	100.0%

Value	Count	Frequency (%)
(unknown)	12091	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1357	11.4%
r	957	8.0%
e	848	7.1%
a	826	6.9%
n	659	5.5%
i	659	5.5%
s	626	5.2%
M	569	4.8%
l	530	4.4%
o	491	4.1%
Other values (50)	4430	37.1%

Value	Count	Frequency (%)
	1371	11.3%
r	964	8.0%
a	839	6.9%
e	837	6.9%
i	699	5.8%
s	646	5.3%
n	635	5.3%
l	557	4.6%
M	556	4.6%
o	487	4.0%
Other values (49)	4500	37.2%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	11952	100.0%

Value	Count	Frequency (%)
(unknown)	12091	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1357	11.4%
r	957	8.0%
e	848	7.1%
a	826	6.9%
n	659	5.5%
i	659	5.5%
s	626	5.2%
M	569	4.8%
l	530	4.4%
o	491	4.1%
Other values (50)	4430	37.1%

Value	Count	Frequency (%)
	1371	11.3%
r	964	8.0%
a	839	6.9%
e	837	6.9%
i	699	5.8%
s	646	5.3%
n	635	5.3%
l	557	4.6%
M	556	4.6%
o	487	4.0%
Other values (49)	4500	37.2%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	11952	100.0%

Value	Count	Frequency (%)
(unknown)	12091	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1357	11.4%
r	957	8.0%
e	848	7.1%
a	826	6.9%
n	659	5.5%
i	659	5.5%
s	626	5.2%
M	569	4.8%
l	530	4.4%
o	491	4.1%
Other values (50)	4430	37.1%

Value	Count	Frequency (%)
	1371	11.3%
r	964	8.0%
a	839	6.9%
e	837	6.9%
i	699	5.8%
s	646	5.3%
n	635	5.3%
l	557	4.6%
M	556	4.6%
o	487	4.0%
Other values (49)	4500	37.2%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	286
female	160

male	293
female	153

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.7174888	4.6860987
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2104	2090
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	male
3rd row	female	female
4th row	male	female
5th row	male	male

Common Values

Value	Count	Frequency (%)
male	286	64.1%
female	160	35.9%

Value	Count	Frequency (%)
male	293	65.7%
female	153	34.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	286	64.1%
female	160	35.9%

Value	Count	Frequency (%)
male	293	65.7%
female	153	34.3%

Most occurring characters

Value	Count	Frequency (%)
e	606	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	160	7.6%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2104	100.0%

Value	Count	Frequency (%)
(unknown)	2090	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	606	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	160	7.6%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2104	100.0%

Value	Count	Frequency (%)
(unknown)	2090	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	606	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	160	7.6%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2104	100.0%

Value	Count	Frequency (%)
(unknown)	2090	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	606	28.8%
m	446	21.2%
a	446	21.2%
l	446	21.2%
f	160	7.6%

Value	Count	Frequency (%)
e	599	28.7%
m	446	21.3%
a	446	21.3%
l	446	21.3%
f	153	7.3%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	81	79
Distinct (%)	22.9%	21.9%
Missing	93	85
Missing (%)	20.9%	19.1%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	30.459178	28.614044

	Dataset A	Dataset B
Minimum	0.42	0.42
Maximum	80	80
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.42
5-th percentile	4	3
Q1	21	19
median	29	27
Q3	39	36
95-th percentile	59.4	56
Maximum	80	80
Range	79.58	79.58
Interquartile range (IQR)	18	17

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	15.111016	14.972679
Coefficient of variation (CV)	0.49610716	0.52326329
Kurtosis	0.2718943	0.46267955
Mean	30.459178	28.614044
Median Absolute Deviation (MAD)	9	8
Skewness	0.47975377	0.4756362
Sum	10752.09	10329.67
Variance	228.34282	224.18112
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
22	15	3.4%
18	14	3.1%
28	14	3.1%
24	14	3.1%
30	13	2.9%
19	13	2.9%
32	12	2.7%
16	11	2.5%
36	11	2.5%
26	11	2.5%
Other values (71)	225	50.4%
(Missing)	93	20.9%

Value	Count	Frequency (%)
30	16	3.6%
19	15	3.4%
27	14	3.1%
25	13	2.9%
26	13	2.9%
36	13	2.9%
18	12	2.7%
28	11	2.5%
29	11	2.5%
24	11	2.5%
Other values (69)	232	52.0%
(Missing)	85	19.1%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
1	3	0.7%
2	5	1.1%
3	3	0.7%
4	4	0.9%
5	1	0.2%
6	1	0.2%
7	1	0.2%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
0.92	1	0.2%
1	4	0.9%
2	8	1.8%
3	4	0.9%
4	6	1.3%
5	3	0.7%
7	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	2	0.4%
0.83	1	0.2%
0.92	1	0.2%
1	4	0.9%
2	8	1.8%
3	4	0.9%
4	6	1.3%
5	3	0.7%
7	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.67	1	0.2%
0.75	2	0.4%
1	3	0.7%
2	5	1.1%
3	3	0.7%
4	4	0.9%
5	1	0.2%
6	1	0.2%
7	1	0.2%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.4955157	0.54932735

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	310	301
Zeros (%)	69.5%	67.5%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	2	3
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.0594582	1.1202074
Coefficient of variation (CV)	2.1380922	2.0392348
Kurtosis	19.023477	14.967694
Mean	0.4955157	0.54932735
Median Absolute Deviation (MAD)	0	0
Skewness	3.7702281	3.3831567
Sum	221	245
Variance	1.1224518	1.2548647
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	310	69.5%
1	99	22.2%
2	17	3.8%
4	7	1.6%
3	7	1.6%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	301	67.5%
1	104	23.3%
2	14	3.1%
4	11	2.5%
3	10	2.2%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	310	69.5%
1	99	22.2%
2	17	3.8%
3	7	1.6%
4	7	1.6%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	301	67.5%
1	104	23.3%
2	14	3.1%
3	10	2.2%
4	11	2.5%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	301	67.5%
1	104	23.3%
2	14	3.1%
3	10	2.2%
4	11	2.5%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	310	69.5%
1	99	22.2%
2	17	3.8%
3	7	1.6%
4	7	1.6%
5	3	0.7%
8	3	0.7%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.39237668	0.39237668

	Dataset A	Dataset B
Minimum	0	0
Maximum	6	6
Zeros	340	335
Zeros (%)	76.2%	75.1%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	6	6
Range	6	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.85108534	0.80494829
Coefficient of variation (CV)	2.1690518	2.0514682
Kurtosis	10.949782	10.338209
Mean	0.39237668	0.39237668
Median Absolute Deviation (MAD)	0	0
Skewness	2.9315905	2.7294591
Sum	175	175
Variance	0.72434625	0.64794175
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	340	76.2%
1	58	13.0%
2	39	8.7%
5	3	0.7%
4	3	0.7%
3	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	335	75.1%
1	61	13.7%
2	44	9.9%
3	2	0.4%
5	2	0.4%
4	1	0.2%
6	1	0.2%

Value	Count	Frequency (%)
0	340	76.2%
1	58	13.0%
2	39	8.7%
3	2	0.4%
4	3	0.7%
5	3	0.7%
6	1	0.2%

Value	Count	Frequency (%)
0	335	75.1%
1	61	13.7%
2	44	9.9%
3	2	0.4%
4	1	0.2%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	335	75.1%
1	61	13.7%
2	44	9.9%
3	2	0.4%
4	1	0.2%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	340	76.2%
1	58	13.0%
2	39	8.7%
3	2	0.4%
4	3	0.7%
5	3	0.7%
6	1	0.2%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	383	380
Distinct (%)	85.9%	85.2%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7959641	6.8318386
Min length	3	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	3031	3047
Distinct characters	31	35
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	334	333 ?
Unique (%)	74.9%	74.7%

Sample

	Dataset A	Dataset B
1st row	31027	239865
2nd row	2686	28424
3rd row	PC 17477	35851
4th row	2624	17421
5th row	693	244361

Value	Count	Frequency (%)
pc	28	5.0%
c.a	16	2.8%
a/5	9	1.6%
ca	7	1.2%
w./c	7	1.2%
soton/o.q	7	1.2%
ston/o	6	1.1%
2	6	1.1%
1601	4	0.7%
2144	4	0.7%
Other values (401)	471	83.4%

Value	Count	Frequency (%)
pc	27	4.7%
c.a	14	2.5%
a/5	8	1.4%
ca	8	1.4%
2	6	1.1%
ston/o	6	1.1%
w./c	5	0.9%
sc/paris	5	0.9%
1601	5	0.9%
347082	5	0.9%
Other values (402)	481	84.4%

Most occurring characters

Value	Count	Frequency (%)
3	372	12.3%
1	338	11.2%
2	296	9.8%
7	247	8.1%
4	240	7.9%
6	221	7.3%
0	201	6.6%
5	195	6.4%
9	155	5.1%
8	146	4.8%
Other values (21)	620	20.5%

Value	Count	Frequency (%)
3	377	12.4%
1	350	11.5%
2	306	10.0%
7	229	7.5%
4	225	7.4%
6	213	7.0%
5	205	6.7%
0	194	6.4%
9	163	5.3%
8	145	4.8%
Other values (25)	640	21.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3031	100.0%

Value	Count	Frequency (%)
(unknown)	3047	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	372	12.3%
1	338	11.2%
2	296	9.8%
7	247	8.1%
4	240	7.9%
6	221	7.3%
0	201	6.6%
5	195	6.4%
9	155	5.1%
8	146	4.8%
Other values (21)	620	20.5%

Value	Count	Frequency (%)
3	377	12.4%
1	350	11.5%
2	306	10.0%
7	229	7.5%
4	225	7.4%
6	213	7.0%
5	205	6.7%
0	194	6.4%
9	163	5.3%
8	145	4.8%
Other values (25)	640	21.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3031	100.0%

Value	Count	Frequency (%)
(unknown)	3047	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	372	12.3%
1	338	11.2%
2	296	9.8%
7	247	8.1%
4	240	7.9%
6	221	7.3%
0	201	6.6%
5	195	6.4%
9	155	5.1%
8	146	4.8%
Other values (21)	620	20.5%

Value	Count	Frequency (%)
3	377	12.4%
1	350	11.5%
2	306	10.0%
7	229	7.5%
4	225	7.4%
6	213	7.0%
5	205	6.7%
0	194	6.4%
9	163	5.3%
8	145	4.8%
Other values (25)	640	21.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3031	100.0%

Value	Count	Frequency (%)
(unknown)	3047	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	372	12.3%
1	338	11.2%
2	296	9.8%
7	247	8.1%
4	240	7.9%
6	221	7.3%
0	201	6.6%
5	195	6.4%
9	155	5.1%
8	146	4.8%
Other values (21)	620	20.5%

Value	Count	Frequency (%)
3	377	12.4%
1	350	11.5%
2	306	10.0%
7	229	7.5%
4	225	7.4%
6	213	7.0%
5	205	6.7%
0	194	6.4%
9	163	5.3%
8	145	4.8%
Other values (25)	640	21.0%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	179	183
Distinct (%)	40.1%	41.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	31.32685	30.19587

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	6	8
Zeros (%)	1.3%	1.8%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.0719	7.15
Q1	7.8958	7.925
median	13.5	15.2458
Q3	30.92395	30.92395
95-th percentile	93.5	86.5
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	23.02815	22.99895

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	50.063661	44.575585
Coefficient of variation (CV)	1.5981071	1.4762146
Kurtosis	40.93267	37.472856
Mean	31.32685	30.19587
Median Absolute Deviation (MAD)	6.2708	7.96455
Skewness	5.3663318	4.998816
Sum	13971.775	13467.358
Variance	2506.3701	1986.9827
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
13	24	5.4%
7.75	24	5.4%
8.05	20	4.5%
7.8958	17	3.8%
10.5	13	2.9%
7.2292	9	2.0%
26.55	9	2.0%
26	8	1.8%
7.925	8	1.8%
7.05	7	1.6%
Other values (169)	307	68.8%

Value	Count	Frequency (%)
8.05	21	4.7%
7.8958	19	4.3%
13	17	3.8%
26	15	3.4%
7.75	15	3.4%
10.5	11	2.5%
7.225	10	2.2%
7.775	10	2.2%
7.925	8	1.8%
26.55	8	1.8%
Other values (173)	312	70.0%

Value	Count	Frequency (%)
0	6	1.3%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.75	2	0.4%
6.95	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%
7.05	7	1.6%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	8	1.8%
4.0125	1	0.2%
6.2375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	2	0.4%
6.975	1	0.2%
7.0458	1	0.2%
7.05	3	0.7%
7.0542	1	0.2%

Value	Count	Frequency (%)
0	8	1.8%
4.0125	1	0.2%
6.2375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	2	0.4%
6.975	1	0.2%
7.0458	1	0.2%
7.05	3	0.7%
7.0542	1	0.2%

Value	Count	Frequency (%)
0	6	1.3%
6.2375	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.75	2	0.4%
6.95	1	0.2%
6.975	1	0.2%
7.0458	1	0.2%
7.05	7	1.6%
7.0542	2	0.4%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	88	82
Distinct (%)	89.8%	86.3%
Missing	348	351
Missing (%)	78.0%	78.7%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	15
Median length	3	3
Mean length	3.4591837	3.3473684
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	339	318
Distinct characters	19	19
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	78	72 ?
Unique (%)	79.6%	75.8%

Sample

	Dataset A	Dataset B
1st row	B35	C68
2nd row	C110	B18
3rd row	E24	E24
4th row	E31	E67
5th row	B69	F4

Value	Count	Frequency (%)
c92	2	1.8%
f4	2	1.8%
e101	2	1.8%
e44	2	1.8%
c126	2	1.8%
f33	2	1.8%
c124	2	1.8%
c123	2	1.8%
c78	2	1.8%
e33	2	1.8%
Other values (89)	90	81.8%

Value	Count	Frequency (%)
d	3	2.8%
c22	3	2.8%
c26	3	2.8%
f2	3	2.8%
b77	2	1.9%
b5	2	1.9%
c2	2	1.9%
e8	2	1.9%
b22	2	1.9%
f4	2	1.9%
Other values (83)	84	77.8%

Most occurring characters

Value	Count	Frequency (%)
C	35	10.3%
3	32	9.4%
2	31	9.1%
1	30	8.8%
4	24	7.1%
B	24	7.1%
6	23	6.8%
5	18	5.3%
7	18	5.3%
E	17	5.0%
Other values (9)	87	25.7%

Value	Count	Frequency (%)
2	40	12.6%
B	30	9.4%
C	29	9.1%
6	24	7.5%
8	21	6.6%
5	21	6.6%
1	20	6.3%
D	17	5.3%
7	17	5.3%
4	17	5.3%
Other values (9)	82	25.8%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	339	100.0%

Value	Count	Frequency (%)
(unknown)	318	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
C	35	10.3%
3	32	9.4%
2	31	9.1%
1	30	8.8%
4	24	7.1%
B	24	7.1%
6	23	6.8%
5	18	5.3%
7	18	5.3%
E	17	5.0%
Other values (9)	87	25.7%

Value	Count	Frequency (%)
2	40	12.6%
B	30	9.4%
C	29	9.1%
6	24	7.5%
8	21	6.6%
5	21	6.6%
1	20	6.3%
D	17	5.3%
7	17	5.3%
4	17	5.3%
Other values (9)	82	25.8%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	339	100.0%

Value	Count	Frequency (%)
(unknown)	318	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
C	35	10.3%
3	32	9.4%
2	31	9.1%
1	30	8.8%
4	24	7.1%
B	24	7.1%
6	23	6.8%
5	18	5.3%
7	18	5.3%
E	17	5.0%
Other values (9)	87	25.7%

Value	Count	Frequency (%)
2	40	12.6%
B	30	9.4%
C	29	9.1%
6	24	7.5%
8	21	6.6%
5	21	6.6%
1	20	6.3%
D	17	5.3%
7	17	5.3%
4	17	5.3%
Other values (9)	82	25.8%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	339	100.0%

Value	Count	Frequency (%)
(unknown)	318	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
C	35	10.3%
3	32	9.4%
2	31	9.1%
1	30	8.8%
4	24	7.1%
B	24	7.1%
6	23	6.8%
5	18	5.3%
7	18	5.3%
E	17	5.0%
Other values (9)	87	25.7%

Value	Count	Frequency (%)
2	40	12.6%
B	30	9.4%
C	29	9.1%
6	24	7.5%
8	21	6.6%
5	21	6.6%
1	20	6.3%
D	17	5.3%
7	17	5.3%
4	17	5.3%
Other values (9)	82	25.8%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	1
Missing (%)	0.2%	0.2%
Memory size	7.0 KiB	7.0 KiB

S	324
C	76
Q	45

S	324
C	87
Q	34

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	445
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	C	S
3rd row	C	Q
4th row	C	C
5th row	S	S

Common Values

Value	Count	Frequency (%)
S	324	72.6%
C	76	17.0%
Q	45	10.1%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	324	72.6%
C	87	19.5%
Q	34	7.6%
(Missing)	1	0.2%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	324	72.8%
c	76	17.1%
q	45	10.1%

Value	Count	Frequency (%)
s	324	72.8%
c	87	19.6%
q	34	7.6%

Most occurring characters

Value	Count	Frequency (%)
S	324	72.8%
C	76	17.1%
Q	45	10.1%

Value	Count	Frequency (%)
S	324	72.8%
C	87	19.6%
Q	34	7.6%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	324	72.8%
C	76	17.1%
Q	45	10.1%

Value	Count	Frequency (%)
S	324	72.8%
C	87	19.6%
Q	34	7.6%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	324	72.8%
C	76	17.1%
Q	45	10.1%

Value	Count	Frequency (%)
S	324	72.8%
C	87	19.6%
Q	34	7.6%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	445	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	324	72.8%
C	76	17.1%
Q	45	10.1%

Value	Count	Frequency (%)
S	324	72.8%
C	87	19.6%
Q	34	7.6%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.000	0.174	-0.242	0.032	0.242	0.149	-0.168	0.096
Embarked	0.000	1.000	0.196	0.000	0.000	0.257	0.105	0.000	0.220
Fare	0.174	0.196	1.000	0.386	-0.014	0.474	0.209	0.424	0.252
Parch	-0.242	0.000	0.386	1.000	0.046	0.000	0.323	0.477	0.122
PassengerId	0.032	0.000	-0.014	0.046	1.000	0.011	0.083	-0.069	0.152
Pclass	0.242	0.257	0.474	0.000	0.011	1.000	0.096	0.121	0.300
Sex	0.149	0.105	0.209	0.323	0.083	0.096	1.000	0.236	0.482
SibSp	-0.168	0.000	0.424	0.477	-0.069	0.121	0.236	1.000	0.178
Survived	0.096	0.220	0.252	0.122	0.152	0.300	0.482	0.178	1.000

Dataset B

	Age	Embarked	Fare	Parch	PassengerId	Pclass	Sex	SibSp	Survived
Age	1.000	0.114	0.093	-0.323	-0.003	0.253	0.089	-0.255	0.227
Embarked	0.114	1.000	0.091	0.016	0.000	0.221	0.000	0.078	0.103
Fare	0.093	0.091	1.000	0.426	-0.015	0.426	0.143	0.458	0.214
Parch	-0.323	0.016	0.426	1.000	0.016	0.037	0.329	0.500	0.213
PassengerId	-0.003	0.000	-0.015	0.016	1.000	0.000	0.000	-0.037	0.144
Pclass	0.253	0.221	0.426	0.037	0.000	1.000	0.109	0.146	0.303
Sex	0.089	0.000	0.143	0.329	0.000	0.109	1.000	0.198	0.474
SibSp	-0.255	0.078	0.458	0.500	-0.037	0.146	0.198	1.000	0.151
Survived	0.227	0.103	0.214	0.213	0.144	0.303	0.474	0.151	1.000

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset A

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset B

The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
476	477	0	2	Renouf, Mr. Peter Henry	male	34.0	1	0	31027	21.0000	NaN	S
568	569	0	3	Doharr, Mr. Tannous	male	NaN	0	0	2686	7.2292	NaN	C
641	642	1	1	Sagesser, Mlle. Emma	female	24.0	0	0	PC 17477	69.3000	B35	C
522	523	0	3	Lahoud, Mr. Sarkis	male	NaN	0	0	2624	7.2250	NaN	C
545	546	0	1	Nicholson, Mr. Arthur Ernest	male	64.0	0	0	693	26.0000	NaN	S
258	259	1	1	Ward, Miss. Anna	female	35.0	0	0	PC 17755	512.3292	NaN	C
110	111	0	1	Porter, Mr. Walter Chamberlain	male	47.0	0	0	110465	52.0000	C110	S
529	530	0	2	Hocking, Mr. Richard George	male	23.0	2	1	29104	11.5000	NaN	S
98	99	1	2	Doling, Mrs. John T (Ada Julia Bone)	female	34.0	0	1	231919	23.0000	NaN	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
791	792	0	2	Gaskell, Mr. Alfred	male	16.0	0	0	239865	26.0000	NaN	S
191	192	0	2	Carbines, Mr. William	male	19.0	0	0	28424	13.0000	NaN	S
156	157	1	3	Gilnagh, Miss. Katherine "Katie"	female	16.0	0	0	35851	7.7333	NaN	Q
581	582	1	1	Thayer, Mrs. John Borland (Marian Longstreth Morris)	female	39.0	1	1	17421	110.8833	C68	C
343	344	0	2	Sedgwick, Mr. Charles Frederick Waddington	male	25.0	0	0	244361	13.0000	NaN	S
29	30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
747	748	1	2	Sinkkonen, Miss. Anna	female	30.0	0	0	250648	13.0000	NaN	S
98	99	1	2	Doling, Mrs. John T (Ada Julia Bone)	female	34.0	0	1	231919	23.0000	NaN	S
542	543	0	3	Andersson, Miss. Sigrid Elisabeth	female	11.0	4	2	347082	31.2750	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
619	620	0	2	Gavey, Mr. Lawrence	male	26.0	0	0	31028	10.5000	NaN	S
266	267	0	3	Panula, Mr. Ernesti Arvid	male	16.0	4	1	3101295	39.6875	NaN	S
615	616	1	2	Herman, Miss. Alice	female	24.0	1	2	220845	65.0000	NaN	S
564	565	0	3	Meanwell, Miss. (Marion Ogden)	female	NaN	0	0	SOTON/O.Q. 392087	8.0500	NaN	S
288	289	1	2	Hosono, Mr. Masabumi	male	42.0	0	0	237798	13.0000	NaN	S
706	707	1	2	Kelly, Mrs. Florence "Fannie"	female	45.0	0	0	223596	13.5000	NaN	S
125	126	1	3	Nicola-Yarred, Master. Elias	male	12.0	1	0	2651	11.2417	NaN	C
280	281	0	3	Duane, Mr. Frank	male	65.0	0	0	336439	7.7500	NaN	Q
517	518	0	3	Ryan, Mr. Patrick	male	NaN	0	0	371110	24.1500	NaN	Q
123	124	1	2	Webber, Miss. Susan	female	32.5	0	0	27267	13.0000	E101	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
854	855	0	2	Carter, Mrs. Ernest Courtenay (Lilian Hughes)	female	44.0	1	0	244252	26.0000	NaN	S
506	507	1	2	Quick, Mrs. Frederick Charles (Jane Richards)	female	33.0	0	2	26360	26.0000	NaN	S
480	481	0	3	Goodwin, Master. Harold Victor	male	9.0	5	2	CA 2144	46.9000	NaN	S
328	329	1	3	Goldsmith, Mrs. Frank John (Emily Alice Brown)	female	31.0	1	1	363291	20.5250	NaN	S
646	647	0	3	Cor, Mr. Liudevit	male	19.0	0	0	349231	7.8958	NaN	S
362	363	0	3	Barbara, Mrs. (Catherine David)	female	45.0	0	1	2691	14.4542	NaN	C
427	428	1	2	Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall")	female	19.0	0	0	250655	26.0000	NaN	S
730	731	1	1	Allen, Miss. Elisabeth Walton	female	29.0	0	0	24160	211.3375	B5	S
352	353	0	3	Elias, Mr. Tannous	male	15.0	1	1	2695	7.2292	NaN	C
498	499	0	1	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.0	1	2	113781	151.5500	C22 C26	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)